Efficacy of mHealth Interventions for Improving Maternal and Neonatal Outcomes Among Pregnant Women With Hypertensive Disorders: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Hypertension is one of the most prevalent medical conditions that arise during pregnancy, resulting in maternal and neonatal complications. Mobile health (mHealth) has emerged as an innovative intervention for delivering maternal and child health care services. The evidence on the effectiveness of mHealth interventions in improving the health outcomes of pregnant women with hypertensive disorders is lacking. Therefore, there is a need for evidence synthesis using systematic review methods to address this evidence gap. OBJECTIVE: This review aims to determine the efficacy of mHealth interventions in improving maternal and neonatal outcomes among pregnant women with hypertensive disorders. The review will answer the following research questions: (1) What are the types of mHealth interventions used in pregnant women with hypertensive disorders? (2) Are the various mHealth interventions effective in improving maternal and neonatal health outcomes, health behaviors, and their knowledge of the disease? and (3) Are mHealth interventions effective in supporting health care providers to make health care decisions for pregnant women with hypertensive disorders? METHODS: This review will include randomized controlled trials, nonrandomized controlled trials, and cohort studies focusing on mHealth interventions for pregnant women with hypertensive disorders. Studies reporting health care providers use of mHealth interventions in caring for pregnant women with hypertensive disorders will be included. The search strategy will be tailored to each database using database-specific search terms. The search will be conducted in PubMed-MEDLINE, ProQuest, CINAHL, Scopus, Web of Science, and CENTRAL. Other literature sources, such as trial registries and bibliographies of relevant studies, will be additionally searched. Studies published in English from January 2000 to January 2023 will be included. A total of 2 review authors will independently perform the data extraction and the quality appraisal. For quality appraisal of randomized controlled trials, the Cochrane Risk of Bias 2 tool will be used. The Risk of Bias in Nonrandomized Studies of Interventions (ROBINS-1) tool will be used for nonrandomized controlled trials, and the Critical Appraisal Skills Programme checklist for cohort studies will be used. Any disagreements between the 2 reviewers will be resolved through discussion and a third reviewer if required. A meta-analysis will be performed based on the availability of the data. RESULTS: As per the protocol, the study methodology was followed, and 2 independent reviewers conducted the search in 6 databases and clinical registries. Currently, the review is in the full-text screening stage. The review will publish the results in the first quarter of 2024. CONCLUSIONS: The evidence synthesized from this systematic review will help guide future research, support health care decisions, and inform policy makers on the effectiveness of mHealth interventions in improving the maternal and neonatal outcomes of pregnant women with hypertensive disorders. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/51792.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.074 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.022 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".